A SVM based classification method for homogeneous data

Huan Li, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)

Abstract

A novel classification method based on SVM is proposed for binary classification tasks of homogeneous data in this paper. The proposed method can effectively predict the binary labeling of the sequence of observation samples in the test set by using the following procedure: we first make different assumptions about the class labeling of this sequence, then we utilize SVM to obtain two classification errors respectively for each assumption, and finally the binary labeling is determined by comparing the obtained two classification errors. The proposed method leverages the homogeneity within the same classes and exploits the difference between different classes, and hence can achieve the effective classification for homogeneous data. Experimental results indicate the power of the proposed method.
Original languageEnglish
Pages (from-to)228-235
Number of pages8
JournalApplied Soft Computing Journal
Volume36
DOIs
Publication statusPublished - 11 Aug 2015

Keywords

  • Homogeneous data
  • Multi-observation samples
  • SVM classification

ASJC Scopus subject areas

  • Software

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